Hi Lalwani,
But I need to augment the directory specific data to every record of that
directory.
Once I have read the data, there is no link back to the directory in the
data which I can use to augment additional data

On Wed, May 5, 2021 at 10:41 PM Lalwani, Jayesh <jlalw...@amazon.com.invalid>
wrote:

> You don’t have to union multiple RDDs.  You can read files from multiple
> directories in a single read call. Spark will manage partitioning of the
> data across directories.
>
>
>
> *From: *Kapil Garg <kapi...@flipkart.com.INVALID>
> *Date: *Wednesday, May 5, 2021 at 10:45 AM
> *To: *spark users <user@spark.apache.org>
> *Subject: *[EXTERNAL] How to read multiple HDFS directories
>
>
>
> *CAUTION*: This email originated from outside of the organization. Do not
> click links or open attachments unless you can confirm the sender and know
> the content is safe.
>
>
>
> Hi,
>
> I am facing issues while reading multiple HDFS directories. Please read
> the problem statement and current approach below
>
>
>
> *Problem Statement*
>
> There are N HDFS directories each having K files. We want to read data
> from all directories such that when we read data from directory D, we map
> all the data and augment it with additional information specific to that
> directory.
>
>
>
> *Current Approach*
>
> In current approach, we are iterating over the directories, reading it in
> RDD, mapping the RDD and the putting the RDD into a list.
>
> After all N directories have been read, we have a list of N RDDs
>
> We call spark Union on the list to merge them together.
>
>
>
> This approach is causing data skewness because there is 1 directory of
> size 12 GBs whereas other RDDs are less than 1 GB. So when the large RDD's
> turn comes, spark submits its task on available executors causing the RDD
> to present on few executors instead of spreading on all.
>
>
>
> Is there a way to avoid this data skewness ? I couldn't find any RDD API,
> spark config which could enforce the data reading tasks evenly on all
> executors.
>
>
> --
>
> Regards
> Kapil Garg
>
>
>
>
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-- 
Regards
Kapil Garg

-- 


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